Vol. 7 No. 01 (2025): Volume 07 Issue 01

Price forecasting plays a crucial role in empowering farmers by providing them with information to make informed decisions regarding crop production and market timing. This study explores the impact of price forecasting on brinjal farmers in Eastern Uttar Pradesh, a region heavily reliant on agriculture. By analyzing historical price data and using forecasting models, the research aims to predict future brinjal prices and assess how these predictions influence farmers' decision-making processes. The findings indicate that access to accurate price forecasts enables farmers to optimize their planting schedules, reduce financial risks, and enhance profitability. The study highlights the potential of price forecasting as a tool for empowering farmers, improving market efficiency, and promoting agricultural sustainability in the region.

This study explores the application of machine learning models for predicting financial risk and optimizing portfolio management. We compare various machine learning algorithms, including Random Forest, Gradient Boosting, Long Short-Term Memory (LSTM), and Transformer networks, to assess their effectiveness in forecasting asset returns, managing risk, and enhancing portfolio performance. The results demonstrate that machine learning models significantly outperform traditional financial models in terms of prediction accuracy and risk-adjusted returns. Notably, LSTM and Transformer models excel at capturing long-term dependencies in financial data, leading to more robust predictions and improved portfolio outcomes. Feature selection and preprocessing were crucial in maximizing model performance. Portfolio optimization using machine learning models, when combined with traditional optimization techniques, resulted in superior Sharpe and Sortino ratios. These findings highlight the potential of machine learning to enhance real-time financial decision-making, offering more adaptive and resilient strategies for managing investment portfolios in dynamic market environments. This research provides valuable insights into the integration of machine learning for financial risk prediction and portfolio management, with implications for future advancements in the field.

This article provides a detailed analysis of modern trends in technological innovations within the field of marine engineering, aimed at transitioning to more sustainable fleet management methods. The focus is placed on the implementation of alternative fuels, digital solutions for navigation optimization and vessel system control, as well as the development of intelligent energy systems that reduce fuel consumption and greenhouse gas emissions. A significant component is the digitalization of the maritime sector, encompassing the use of big data, blockchain technologies, digital twins, and intelligent routing systems. This approach enables comprehensive vessel monitoring, increased transparency of supply chains, and more accurate operational risk forecasting.

Particular attention is given to the role of port infrastructure, as its modernization is critical for the adoption of low-carbon fuels and the establishment of "green corridors." Additionally, the importance of training personnel capable of effectively utilizing innovative technologies is emphasized, along with the need for harmonizing legal frameworks that regulate digital data exchange. The comprehensive strategy proposed in the article integrates technical and organizational aspects and aims to strengthen the economic potential of maritime transport while adhering to principles of environmental responsibility.

Thus, this study provides a thorough overview of the prospects and challenges associated with the development of technological solutions for shipping and proposes ways to address them through an interdisciplinary approach and collaboration among all stakeholders in the industry.

The article provides suggestions on how to develop innovative insurance products by insurance companies in the development of the insurance services market and organize the sale of E-policies to policyholders using modern information technologies.